System, method, and computer software for the presentation and storage of analysis results

ABSTRACT

A computer program product, and related systems and methods, are described that processes emission intensity data corresponding to probes of a biological probe array. The computer program includes a genotype and statistical analysis manager that determines absolute or relative expression values based, at least in part, on a statistical measure of the emission intensity data and at least one user-selectable statistical parameter. The analysis manager may also determine genotype calls for one or more probes based, at least in part, on the emission intensity data. The analysis manager may further display the absolute or relative expression values based, at least in part, on at least one user-selectable display parameter and/or a measure of normalized change between genotype calls. The measure of normalized change may be based, at least in part, on a comparison of genotype calls and a reference value.

RELATED APPLICATIONS

The present application is a divisional application of U.S. applicationSer. No. 11/352,008, filed Feb. 10, 2006, which is divisionalapplication of and claims priority to U.S. application Ser. No.10/219,882, titled “Method, System, and Computer Software for thePresentation and Storage of Analysis Results”, filed Aug. 15, 2002,which claims priority to U.S. Provisional Patent Application No.60/312,906, titled “METHODS AND SYSTEMS FOR EVALUATING ALLELIC IMBALANCEAND PERFORMING OTHER GENOMIC ANALYSIS FUNCTIONS” filed Aug. 16, 2001,each of which is hereby incorporated by reference herein in its entiretyfor all purposes.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

1. Field of the Invention

The present invention relates to the field of bioinformatics. Inparticular, the present invention relates to computer systems, methods,and products for the storage and presentation of data resulting from theanalysis of microarrays of biological materials.

2. Related Art

Research in molecular biology, biochemistry, and many related healthfields increasingly requires organization and analysis of complex datagenerated by new experimental techniques. The rapidly evolving field ofbioinformatics addresses these tasks. See, e.g., H. Rashidi and K.Buehler, Bioinformatics Basics: Applications in Biological Science andMedicine (CRC Press, London, 2000); Bioinformatics: A Practical Guide tothe Analysis of Gene and Proteins (B. F. Ouelette and A. D. Bzevanis,eds., Wiley & Sons, Inc.; 2d ed., 2001), both of which are herebyincorporated herein by reference in their entireties. Broadly, one areaof bioinformatics applies computational techniques to large genomicdatabases, often distributed over and accessed through networks such asthe Internet, for the purpose of illuminating relationships among genestructure and/or location, protein function, and metabolic processes.

The expanding use of microarray technology is one of the forces drivingthe development of bioinformatics. Spotted arrays, such as those madeusing the Affymetrix® 417™ or 427™ Arrayer from Affymetrix, Inc. ofSanta Clara, Calif., are used to generate information about biologicalsystems. Also, synthesized probe arrays, such as Affymetrix® GeneChip®arrays, have been widely used to generate unprecedented amounts ofinformation about biological systems. For example, the GeneChip® HumanGenome U133 Set (HG-U133A and HG-U133B) is made up of two microarrayscontaining over 1,000,000 unique oligonucleotide features covering morethan 39,000 transcript variants that represent more than 33,000 humangenes. Experimenters can quickly design follow-on experiments withrespect to genes, EST's, or other biological materials of interest by,for example, producing in their own laboratories microscope slidescontaining dense arrays of probes using the Affymetrix® 417™ or 427™Arrayer, or other spotting device.

Analysis of data from experiments with synthesized and/or spotted probearrays may lead to the development of new drugs and new diagnostictools. In some applications, this analysis begins with the capture offluorescent signals indicating hybridization of labeled target sampleswith probes on synthesized or spotted probe arrays. The devices used tocapture these signals often are referred to as scanners, an example ofwhich is the Affymetrix® 428™ Scanner.

There is a great demand in the art for methods for organizing, accessingand analyzing the vast amount of information collected by scanningmicroarrays. Computer-based systems and methods have been developed toassist a user to obtain, analyze, and visualize the vast amounts ofinformation generated by the scanners. These commercial and academicsoftware applications typically provide such information as intensitiesof hybridization reactions or comparisons of hybridization reactions.This information may be displayed to a user in graphical form. Inparticular, data representing detected emissions conventionally arestored in a memory device of a computer for processing. The processedimages may be presented to a user on a video monitor or other device,and/or operated upon by various data processing products or systems.

In particular, microarrays and associated instrumentation and computersystems have been developed for rapid and large-scale collection of dataabout the expression of genes or expressed sequence tags (EST's) intissue samples. The data may be used, among other things, to studygenetic characteristics and to detect mutations relevant to genetic andother diseases or conditions. More specifically, the data gained throughmicroarray experiments is valuable to researchers because, among otherreasons, many disease states can potentially be characterized bydifferences in the expression levels of various genes, either throughchanges in the copy number of the genetic DNA or through changes inlevels of transcription (e.g., through control of initiation, provisionof RNA precursors, or RNA processing) of particular genes. Thus, forexample, researchers use microarrays to answer questions such as: Whichgenes are expressed in cells of a malignant tumor but not expressed ineither healthy tissue or tissue treated according to a particularregime? Which genes or EST's are expressed in particular organs but notin others? Which genes or EST's are expressed in particular species butnot in others? How does the environment, drugs, or other factorsinfluence gene expression? Data collection is only an initial step,however, in answering these and other questions. Researchers areincreasingly challenged to extract biologically meaningful informationfrom the vast amounts of data generated by microarray technologies, andto design follow-on experiments. A need exists to provide researcherswith improved tools and information to perform these tasks.

SUMMARY OF THE INVENTION

Systems, methods, and computer program products are described herein toaddress these and other needs. In accordance with one embodiment, amethod is described that includes receiving first emission intensitydata and second emission intensity data corresponding to probes of aprobe array; determining first and second genotype calls for one or moreprobe sets, each having one or more probes, based, at least in part, onthe first and second emission intensity data; comparing a first of thefirst genotype calls with a corresponding first of the second genotypecalls and with a reference value; and displaying a measure of normalizedchange between the first and second genotype calls based, at least inpart, on the comparison of first and second genotype calls and referencevalue. The emission intensity data may include a statistical measure ofpixel values corresponding to the probes. The probe array may include asynthesized probe array or a spotted probe array. The genotype call mayinclude a biallelic call, which may include combinations of two alleles.Also, the biallelic call may include a relative allele signal thatincludes a numerical value between a range, wherein calls near oneextreme of the range correspond to one type of homozygous call, callsnear the opposing extreme of the range correspond to a second type ofhomozygous call, and intermediate calls in an intermediate sub-rangewithin the range correspond to a heterozygous call. The reference valuemay include a standard deviation value.

In this and other embodiments, the step of displaying a measure ofnormalized change may include displaying a graphical user interface,which may display information in text and/or graphical formats. In someimplementations, the graphical user interface includes one or moreassociations of identification data with the measure of normalizedchange. The identification data may include probe set identifiers, oneor more SNP locations, one or more genotype calls, one or more relativeallele signals, or any combination thereof. The one or more SNPlocations may include chromosome number and/or estimated geneticdistance. For example, the estimated genetic distance may be a relativemeasure of a distance from a SNP location to the top of the short arm ofa chromosome, such as may be expressed in centimorgans. Theidentification data may be displayed in a geometric association with themeasure of normalized change, such as by columns or rows of graphical ortextual elements. The identification data may also, or in thealternative, be displayed in a color, shade, or intensity associationwith the measure of normalized change.

In accordance with a further embodiment, a method is described thatincludes receiving first emission intensity data and second emissionintensity data corresponding to probes of a probe array, wherein thefirst and second emission intensity data include a statistical measureof pixel values corresponding to the probes; determining first andsecond genotype calls for one or more probe sets, each having one ormore probes based, at least in part, on the first and second emissionintensity data; comparing a first of the first genotype calls with acorresponding first of the second genotype calls; and displaying ameasure of normalized change between the first and second genotypecalls. The measure of normalized change may be based, at least in part,on the comparison of first and second genotype calls and referencevalue.

A computer program product is described in accordance with anotherembodiment. The product includes an input manager that receives firstemission intensity data and second emission intensity data correspondingto probes of a probe array; a genotype analysis determiner thatdetermines first and second genotype calls for one or more probe sets,each having one or more probes based, at least in part, on the first andsecond emission intensity data; a genotype comparator that compares afirst of the first genotype calls with a corresponding first of thesecond genotype calls and with a reference value; and an output managerthat displays a measure of normalized change between the first andsecond genotype calls. The measure of normalized change may be based, atleast in part, on the comparison of first and second genotype calls andreference value.

In accordance with yet another embodiment, a method is described thatincludes receiving one or more sets of emission intensity datacorresponding to probes of a biological probe array; determiningabsolute or relative expression values based, at least in part, on astatistical measure of the emission intensity data and at least oneuser-selectable statistical parameter; and displaying the absolute orrelative expression values based, at least in part, on at least oneuser-selectable display parameter.

A computer program product is described in accordance with a furtherembodiment. The product includes an input manager that receives one ormore sets of emission intensity data corresponding to probes of abiological probe array; a statistical analysis determiner thatdetermines absolute or relative expression values based, at least inpart, on a statistical measure of the emission intensity data and atleast one user-selectable statistical parameter; and an output managerthat displays the absolute or relative expression values based, at leastin part, on at least one user-selectable display parameter. Inaccordance with yet a further embodiment, a computer program product,includes an input manager that receives one or more sets of emissionintensity data corresponding to probes of a biological probe array, anda genotype and statistical analysis manager. The manager determinesabsolute or relative expression values based, at least in part, on astatistical measure of the emission intensity data and at least oneuser-selectable statistical parameter, and is further constructed andarranged, when the one or more sets of emission intensity data includefirst and second sets of emission intensity data, to determine first andsecond genotype calls for one or more probe sets, each having one ormore probes based, at least in part, on the first and second sets ofemission intensity data, and is yet further constructed and arranged todisplay the absolute or relative expression values based, at least inpart, on at least one user-selectable display parameter and a measure ofnormalized change between the first and second genotype calls based, atleast in part, on the comparison of first and second genotype calls andreference value.

In accordance with another embodiment, a system is described thatincludes a scanner constructed and arranged to provide emissionintensity data corresponding to probes of a biological probe array. Thesystem also has a computer constructed and arranged to execute acomputer program product including an input manager that receives one ormore sets of the emission intensity data. The computer program productalso has a genotype and statistical analysis manager constructed andarranged to determine absolute or relative expression values based, atleast in part, on a statistical measure of the emission intensity dataand at least one user-selectable statistical parameter. The manager isfurther constructed and arranged, when the one or more sets of emissionintensity data include first and second sets of emission intensity data,to determine first and second genotype calls for one or more probe sets,each having one or more probes based, at least in part, on the first andsecond sets of emission intensity data. The manager is yet furtherconstructed and arranged to display the absolute or relative expressionvalues based, at least in part, on at least one user-selectable displayparameter and a measure of normalized change between the first andsecond genotype calls based, at least in part, on the comparison offirst and second genotype calls and reference value.

The above implementations are not necessarily inclusive or exclusive ofeach other and may be combined in any manner that is non-conflicting andotherwise possible, whether they be presented in association with asame, or a different, aspect or implementation. The description of oneimplementation is not intended to be limiting with respect to otherimplementations. Also, any one or more function, step, operation, ortechnique described elsewhere in this specification may, in alternativeimplementations, be combined with any one or more function, step,operation, or technique described in the summary. Thus, the aboveimplementations are illustrative rather than limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference numerals indicate like structures ormethod steps and the leftmost digit of a reference numeral indicates thenumber of the figure in which the referenced element first appears (forexample, the element 120 appears first in FIG. 1). In functional blockdiagrams, rectangles generally indicate functional elements,parallelograms generally indicate data, and rectangles with a pair ofdouble borders generally indicate predefined functional elements. Theseconventions, however, are intended to be typical or illustrative, ratherthan limiting.

FIG. 1 is a functional block diagram of one implementation of alaboratory information management system that is connected to aplurality of user computers via a network;

FIG. 2 is a functional block diagram of the laboratory informationmanagement system and user computers of FIG. 1 including illustrativeembodiments of a scanner and hybridized probe arrays;

FIG. 3 is a functional block diagram of one implementation of a usercomputer system of FIGS. 1 and 2 including illustrative embodiments ofprobe-array analysis executables and display/output devices includinggraphical user interfaces;

FIG. 4 is a functional block diagram of the probe-array analysisexecutables of FIG. 3 including one implementation of a genotype andstatistical analysis manager;

FIGS. 5A and 5B are graphical illustration of particular implementationsof the report data file of FIG. 4; and

FIG. 6 is a graphical illustration of a particular implementation of theanalysis output data file of FIG. 4.

DETAILED DESCRIPTION

Systems, methods, and computer products are now described with referenceto an illustrative embodiment referred to as genotype and statisticalanalysis manager 400. Manager 400 is shown in a computer systemenvironment in FIG. 4 with examples of graphical user interface outputpresented in FIGS. 5A, 5B and 6. In a typical implementation, manager400 may be used to provide a user with information related to resultsfrom experiments with probe arrays. More specifically, manager 400determines absolute or relative expression values based, at least inpart, on a statistical measure of the emission intensity data and atleast one user-selectable statistical parameter. Also, when the one ormore sets of emission intensity data include first and second sets ofemission intensity data, manager 400 determines first and secondgenotype calls for one or more probe sets, each having one or moreprobes based, at least in part, on the first and second sets of emissionintensity data. Further, manager 400 may display the absolute orrelative expression values based, at least in part, on at least oneuser-selectable display parameter and a measure of normalized changebetween the first and second genotype calls based, at least in part, onthe comparison of first and second genotype calls and a reference value.The experiments often involve the use of scanning equipment to detecthybridization of probe-target pairs, and the analysis of detectedhybridization by various software applications. Illustrative systems andsoftware applications suitable for implementation of the presentinvention are now described in relation to FIGS. 1 through 3.

FIG. 1 is a simplified schematic diagram of illustrative systems forgenerating, sharing, and processing data derived from experiments usingprobe arrays, such as illustrative hybridized spotted arrays 172A andhybridized synthesized arrays 172B (generally and collectively referredto as probe arrays 172). In this example, illustrative scanner systems100A and 100B (generally and collectively referred to as scanner system100) are used to scan probe arrays 172. A scanner system 100 typicallymay include a user computer (e.g., user computers 150A and 150B,generally and collectively referred to as user computer 150) and ascanner (e.g., scanners 170A and 170B, generally and collectivelyreferred to as scanner 170). In this example, data may be communicatedbetween user computer 150 and Laboratory Information Management (LIMS)server 120 over network 125. LIMS server 120 and associated softwaregenerally provides data capturing, tracking, and analysis functions froma centralized infrastructure. Aspects of illustrative LIMS are describedin U.S. patent application Ser. Nos. 09/683,912 and 09/682,098; and inU.S. Provisional Patent Application Nos. 60/220,587 and 60/273,231, allof which are hereby incorporated by reference herein for all purposes.LIMS server 120 and network 125 are optional, and the systems in otherimplementations may include a scanner for spotted arrays and notsynthesized arrays, or vice versa. Also, rather than employing separateuser computers 150A and 150B, a single computer may be used in otherimplementations. Further, user computer 150, or any functionalcomponents thereof, may also or in addition be integral to scanner 170in some implementations so that, for example, it is located within thesame housing as the scanner.

More generally, a large variety of computer and/or network architecturesand designs may be employed, and it will be understood by those ofordinary skill in the relevant art that many components of typicalcomputer network systems are not shown in FIG. 1. Components ofillustrative computers are described in greater detail below in relationto FIGS. 2 and 3.

Probe Arrays 172: Various techniques and technologies may be used forsynthesizing dense arrays of biological materials on or in a substrateor support. For example, Affymetrix® GeneChip® arrays are synthesized inaccordance with techniques sometimes referred to as VLSIPS™ (Very LargeScale Immobilized Polymer Synthesis) technologies. Some aspects ofVLSIPS™ and other microarray manufacturing technologies are described inU.S. Pat. Nos. 5,424,186; 5,143,854; 5,445,934; 5,744,305; 5,831,070;5,837,832; 6,022,963; 6,083,697; 6,291,183; 6,309,831; and 6,310,189,all of which are hereby incorporated by reference in their entiretiesfor all purposes. The probes of these arrays in some implementationsconsist of nucleic acids that are synthesized by methods including thesteps of activating regions of a substrate and then contacting thesubstrate with a selected monomer solution. As used herein, nucleicacids may include any polymer or oligomer of nucleosides or nucleotides(polynucleotides or oligonucleotides) that include pyrimidine and/orpurine bases, preferably cytosine, thymine, and uracil, and adenine andguanine, respectively. Nucleic acids may include anydeoxyribonucleotide, ribonucleotide, and/or peptide nucleic acidcomponent, and/or any chemical variants thereof such as methylated,hydroxymethylated or glucosylated forms of these bases, and the like.The polymers or oligomers may be heterogeneous or homogeneous incomposition, and may be isolated from naturally-occurring sources or maybe artificially or synthetically produced. In addition, the nucleicacids may be DNA or RNA, or a mixture thereof, and may exist permanentlyor transitionally in single-stranded or double-stranded form, includinghomoduplex, heteroduplex, and hybrid states. Probes of other biologicalmaterials, such as peptides or polysaccharides as non-limiting examples,may also be formed. For more details regarding possible implementations,see U.S. Pat. No. 6,156,501, which is hereby incorporated by referenceherein in its entirety for all purposes.

A system and method for efficiently synthesizing probe arrays usingmasks is described in U.S. patent application Ser. No. 09/824,931; asystem and method for a rapid and flexible microarray manufacturing andonline ordering system is described in U.S. Provisional PatentApplication Ser. No. 60/265,103; and systems and methods for opticalphotolithography without masks are described in U.S. Pat. No. 6,271,957and in U.S. patent application Ser. No. 09/683,374, all of which arehereby incorporated by reference herein in their entireties for allpurposes.

The probes of synthesized probe arrays typically are used in conjunctionwith biological target molecules of interest, such as cells, proteins,genes or EST's, other DNA sequences, or other biological elements. Morespecifically, the biological molecule of interest may be a ligand,receptor, peptide, nucleic acid (oligonucleotide or polynucleotide ofRNA or DNA), or any other of the biological molecules listed in U.S.Pat. No. 5,445,934 (incorporated by reference above) at column 5, line66 to column 7, line 51. For example, if transcripts of genes are theinterest of an experiment, the target molecules would be thetranscripts. Other examples include protein fragments, small molecules,etc. Target nucleic acid refers to a nucleic acid (often derived from abiological sample) of interest. Frequently, a target molecule isdetected using one or more probes. As used herein, a probe is a moleculefor detecting a target molecule. A probe may be any of the molecules inthe same classes as the target referred to above. As non-limitingexamples, a probe may refer to a nucleic acid, such as anoligonucleotide, capable of binding to a target nucleic acid ofcomplementary sequence through one or more types of chemical bonds,usually through complementary base pairing, usually through hydrogenbond formation. As noted above, a probe may include natural (i.e. A, G,U, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). Inaddition, the bases in probes may be joined by a linkage other than aphosphodiester bond, so long as the bond does not interfere withhybridization. Thus, probes may be peptide nucleic acids in which theconstituent bases are joined by peptide bonds rather than phosphodiesterlinkages. Other examples of probes include antibodies used to detectpeptides or other molecules, any ligands for detecting its bindingpartners. When referring to targets or probes as nucleic acids, itshould be understood that these are illustrative embodiments that arenot to limit the invention in any way.

The samples or target molecules of interest (hereafter, simply targets)are processed so that, typically, they are spatially associated withcertain probes in the probe array. For example, one or more taggedtargets are distributed over the probe array. In accordance with someimplementations, some targets hybridize with probes and remain at theprobe locations, while non-hybridized targets are washed away. Thesehybridized targets, with their tags or labels, are thus spatiallyassociated with the probes. The hybridized probe and target maysometimes be referred to as a probe-target pair. Detection of thesepairs can serve a variety of purposes, such as to determine whether atarget nucleic acid has a nucleotide sequence identical to or differentfrom a specific reference sequence. See, for example, U.S. Pat. No.5,837,832, referred to and incorporated above. Other uses include geneexpression monitoring and evaluation (see, e.g., U.S. Pat. Nos.5,800,992 and 6,040,138, and International Application No.PCT/US98/15151, published as WO99/05323), genotyping (U.S. Pat. No.5,856,092), or other detection of nucleic acids, all of which are herebyincorporated by reference herein in their entireties for all purposes.

Other techniques exist for depositing probes on a substrate or support.For example, “spotted arrays” are commercially fabricated, typically onmicroscope slides. These arrays consist of liquid spots containingbiological material of potentially varying compositions andconcentrations. For instance, a spot in the array may include a fewstrands of short oligonucleotides in a water solution, or it may includea high concentration of long strands of complex proteins. TheAffymetrix® 417™ Arrayer and 427™ Arrayer are devices that depositdensely packed arrays of biological materials on microscope slides inaccordance with these techniques. Aspects of these, and other, spotarrayers are described in U.S. Pat. Nos. 6,040,193 and 6,136,269; inU.S. patent application Ser. No. 09/683,298, in U.S. Provisional PatentApplication No. 60/288,403; and in PCT Application No. PCT/US99/00730(International Publication Number WO 99/36760), all of which are herebyincorporated by reference in their entireties for all purposes. Othertechniques for generating spotted arrays also exist. For example, U.S.Pat. No. 6,040,193 to Winkler, et al. is directed to processes fordispensing drops to generate spotted arrays. The '193 patent, and U.S.Pat. No. 5,885,837 to Winkler, also describe the use of micro-channelsor micro-grooves on a substrate, or on a block placed on a substrate, tosynthesize arrays of biological materials. These patents furtherdescribe separating reactive regions of a substrate from each other byinert regions and spotting on the reactive regions. The '193 and '837patents are hereby incorporated by reference in their entireties.Another technique is based on ejecting jets of biological material toform a spotted array. Other implementations of the jetting technique mayuse devices such as syringes or piezo electric pumps to propel thebiological material. It will be understood that the foregoing arenon-limiting examples of techniques for synthesizing, depositing, orpositioning biological material onto or within a substrate. For example,although a planar array surface is preferred in some implementations ofthe foregoing, a probe array may be fabricated on a surface of virtuallyany shape or even a multiplicity of surfaces. Arrays may comprise probessynthesized or deposited on beads, fibers such as fiber optics, glass orany other appropriate substrate, see U.S. Pat. Nos. 6,361,947,5,770,358, 5,789,162, 5,708,153 and 5,800,992, all of which are herebyincorporated in their entireties for all purposes. Arrays may bepackaged in such a manner as to allow for diagnostics or othermanipulation of in an all inclusive device, see for example, U.S. Pat.Nos. 5,856,174 and 5,922,591 incorporated in their entireties byreference for all purposes.

To ensure proper interpretation of the term “probe” as used herein, itis noted that contradictory conventions exist in the relevantliterature. The word “probe” is used in some contexts to refer not tothe biological material that is synthesized on a substrate or depositedon a slide, as described above, but to what has been referred to hereinas the “target.” To avoid confusion, the term “probe” is used herein torefer to probes such as those synthesized according to the VLSIPS™technology; the biological materials deposited so as to create spottedarrays; and materials synthesized, deposited, or positioned to formarrays according to other current or future technologies. Thus,microarrays formed in accordance with any of these technologies may bereferred to generally and collectively hereafter for convenience as“probe arrays.” Moreover, the term “probe” is not limited to probesimmobilized in array format. Rather, the functions and methods describedherein may also be employed with respect to other parallel assaydevices. For example, these functions and methods may be applied withrespect to probe-set identifiers that identify probes immobilized on orin beads, optical fibers, or other substrates or media.

Probes typically are able to detect the expression of correspondinggenes or EST's by detecting the presence or abundance of mRNAtranscripts present in the target. This detection may, in turn, beaccomplished in some implementations by detecting labeled cRNA that isderived from cDNA derived from the mRNA in the target. In general, agroup of probes, sometimes referred to as a probe set, containssub-sequences in unique regions of the transcripts and does notcorrespond to a full gene sequence. Further details regarding the designand use of probes and probe sets are provided in U.S. Pat. No.6,188,783; in PCT Application Serial No. PCT/US 01/02316, filed Jan. 24,2001; and in U.S. patent application Ser. Nos. 09/721,042, 09/718,295,09/745,965, and 09/764,324, all of which are hereby incorporated hereinby reference in their entireties for all purposes.

Probe Set Identifiers: Probe-set identifiers typically come to theattention of a user, represented by user 275 of FIGS. 2 and 3, as aresult of experiments conducted on probe arrays. For example, user 275may select probe-set identifiers that identify microarray probe setscapable of enabling detection of the expression of mRNA transcripts fromcorresponding genes or EST's of particular interest. As is well known inthe relevant art, an EST is a fragment of a gene sequence that may notbe fully characterized, whereas a gene sequence generally is completeand fully characterized. The word “gene” is used generally herein torefer both to full size genes of known sequence and to computationallypredicted genes. In some implementations, the specific sequencesdetected by the arrays that represent these genes or EST's may bereferred to as, “sequence information fragments (SIF's)” and may berecorded in what may be referred to as a “SIF file.” In particularimplementations, a SIF is a portion of a consensus sequence that hasbeen deemed to best represent the mRNA transcript from a given gene orEST. The consensus sequence may have been derived by comparing andclustering EST's, and possibly also by comparing the EST's to genomicsequence information. A SIF is a portion of the consensus sequence forwhich probes on the array are specifically designed. With respect to theoperations of genotype and statistical analysis manager 400 of theparticular implementation described herein, it is assumed with respectto some aspects that some microarray probe sets may be designed todetect the expression of genes based upon sequences of EST's.

As was described above, the term “probe set” refers in someimplementations to one or more probes from an array of probes on amicroarray. For example, in an Affymetrix® GeneChip® probe array, inwhich probes are synthesized on a substrate, a probe set may consist of30 or 40 probes, half of which typically are controls. These probescollectively, or in various combinations of some or all of them, aredeemed to be indicative of the expression of a gene or EST. In a spottedprobe array, one or more spots may similarly constitute a “probe set.”

The term “probe-set identifiers” is used broadly herein in that a numberof types of such identifiers are possible and may be included within themeaning of this term in various implementations. One type of probe-setidentifier is a name, number, or other symbol that is assigned for thepurpose of identifying a probe set. This name, number, or symbol may bearbitrarily assigned to the probe set by, for example, the manufacturerof the probe array. A user may select this type of probe-set identifierby, for example, highlighting or typing the name. Another type ofprobe-set identifier as intended herein is a graphical representation ofa probe set. For example, dots may be displayed on a scatter plot orother diagram wherein each dot represents a probe set, as described forexample in U.S. Pat. No. 6,420,108, which is hereby incorporated hereinin its entirety for all purposes. Typically, the dot's placement on theplot represents the intensity of the signal from hybridized, tagged,targets (as described in greater detail below) in one or moreexperiments. In these cases, a user may select a probe-set identifier byclicking on, drawing a loop around, or otherwise selecting one or moreof the dots. In another example, user 275 may select a probe-setidentifier by selecting a row or column in a table or spreadsheet thatcorrelates probe sets with accession numbers and other genomicinformation.

Yet another type of probe-set identifier, as that term is used herein,includes a nucleotide or amino acid sequence. For example, it isillustratively assumed that a particular SIF is a unique sequence of 500bases that is a portion of a consensus sequence or exemplar sequencegleaned from EST and/or genomic sequence information. It further isassumed that one or more probe sets are designed to represent the SIF. Auser who specifies all or part of the 500-base sequence thus may beconsidered to have specified all or some of the corresponding probesets.

As a further example with respect to a particular implementation, a usermay specify a portion of the 500-base sequence noted above, which may beunique to that SIF, or, alternatively, may also identify another SIF,EST, cluster of EST's, consensus sequence, and/or gene or protein. Theuser thus specifies a probe-set identifier for one or more genes orEST's. In another variation, it is illustratively assumed that aparticular SIF is a portion of a particular consensus sequence. It isfurther assumed that a user specifies a portion of the consensussequence that is not included in the SIF but that is unique to theconsensus sequence or the gene or EST's the consensus sequence isintended to represent. In that case, the sequence specified by the useris a probe-set identifier that identifies the probe set corresponding tothe SIF, even though the user-specified sequence is not included in theSIF. Parallel cases are possible with respect to user specifications ofpartial sequences of EST's and genes or EST's, as those skilled in therelevant art will now appreciate.

A further example of a probe-set identifier is an accession number of agene or EST. Gene and EST accession numbers are publicly available. Aprobe set may therefore be identified by the accession number or numbersof one or more EST's and/or genes corresponding to the probe set. Thecorrespondence between a probe set and EST's or genes may be maintainedin a suitable database from which the correspondence may be provided tothe user. Similarly, gene fragments or sequences other than EST's may bemapped (e.g., by reference to a suitable database) to correspondinggenes or EST's for the purpose of using their publicly availableaccession numbers as probe-set identifiers. For example, a user may beinterested in product or genomic information related to a particular SIFthat is derived from EST-1 and EST-2. The user may be provided with thecorrespondence between that SIF (or part or all of the sequence of theSIF) and EST-1 or EST-2, or both. To obtain product or genomic datarelated to the SIF, or a partial sequence of it, the user may select theaccession numbers of EST-1, EST-2, or both.

Additional examples of probe-set identifiers include one or more termsthat may be associated with the annotation of one or more gene or ESTsequences, where the gene or EST sequences may be associated with one ormore probe sets. For convenience, such terms may hereafter be referredto as “annotation terms” and will be understood to potentially include,in various implementations, one or more words, graphical elements,characters, or other representational forms that provide informationthat typically is biologically relevant to or related to the gene or ESTsequence. Associations between the probe-set identifier terms and geneor EST sequences may be stored in a database such as a local genomicdatabase, or they may be transferred from one or more remote databases.Examples of such terms associated with annotations include those ofmolecular function (e.g. transcription initiation), cellular location(e.g. nuclear membrane), biological process (e.g. immune response),tissue type (e.g. kidney), or other annotation terms known to those inthe relevant art.

LIMS Server 120: FIG. 2 shows in greater detail a typical configurationof a server computer, such as server 120 of FIG. 1, coupled to aworkstation computer via a network. For convenience, the server computeris referred to herein as LIMS server 120, although this computer maycarry out a variety of functions in addition to those described belowwith respect to LIMS and LIMS-SDK software applications. Moreover, insome implementations any function ascribed to LIMS server 120 may becarried out by one or more other computers, and/or the functions may beperformed in parallel by a group of computers. Network 125 may include alocal area network, a wide area network, the Internet, another network,any combination thereof, or another computer system and networkconfiguration.

Typically, LIMS server 120 is a network-server class of computerdesigned for servicing a number of workstations or other computerplatforms over a network. However, server 120 may be any of a variety oftypes of general-purpose computers such as a personal computer,workstation, main frame computer, or other computer platform now orlater developed. Server 120 typically includes known components such asa processor 205, an operating system 210, a system memory 220, memorystorage devices 225, and input-output controllers 230. It will beunderstood by those skilled in the relevant art that there are manypossible configurations of the components of server 120 and that somecomponents that may typically be included are not shown, such as cachememory, a data backup unit, and many other devices. Similarly, manyhardware and associated software or firmware components that may beimplemented in a network server are not shown in FIG. 2. For example,components to implement one or more firewalls to protect data andapplications, uninterruptable power supplies, LAN switches, web-serverrouting software, and many other components are not shown. Those ofordinary skill in the art will readily appreciate how these and otherconventional components may be implemented.

Processor 205 may include multiple processors; e.g., multiple IntelXeon® 700 MHz. As further examples, processor 205 may include one ormore of a variety of other commercially available processors such asPentium® processors from Intel, SPARC® processors made by SunMicrosystems, or other processors that are or will become available.Processor 205 executes operating system 210, which may be, for example,a Windows®-type operating system (such as Windows® 2000 with SP 1,Windows NT® 4.0 with SP6a) from the Microsoft Corporation; the Solarisoperating system from Sun Microsystems, the Tru64 Unix from Compaq,other Unix® or Linux-type operating systems available from many vendors;another or a future operating system; or some combination thereof.Operating system 210 interfaces with firmware and hardware in awell-known manner, and facilitates processor 205 in coordinating andexecuting the functions of various computer programs that may be writtenin a variety of programming languages. Operating system 210, typicallyin cooperation with processor 205, coordinates and executes functions ofthe other components of server 120. Operating system 210 also providesscheduling, input-output control, file and data management, memorymanagement, and communication control and related services, all inaccordance with known techniques.

System memory 220 may be any of a variety of known or future memorystorage devices. Examples include any commonly available random accessmemory (RAM), magnetic medium such as a resident hard disk or tape, anoptical medium such as a read and write compact disc, or other memorystorage device. Memory storage device 225 may be any of a variety ofknown or future devices, including a compact disk drive, a tape drive, aremovable hard disk drive, or a diskette drive. Such types of memorystorage device 225 typically read from, and/or write to, a programstorage medium (not shown) such as, respectively, a compact disk,magnetic tape, removable hard disk, or floppy diskette. Any of theseprogram storage media, or others now in use or that may later bedeveloped, may be considered a computer program product. As will beappreciated, these program storage media typically store a computersoftware program and/or data. Computer software programs, also calledcomputer control logic, typically are stored in system memory 220 and/orthe program storage device used in conjunction with memory storagedevice 225.

In some embodiments, a computer program product is described comprisinga computer usable medium having control logic (computer softwareprogram, including program code) stored therein. The control logic, whenexecuted by processor 205, causes processor 205 to perform functionsdescribed herein. In other embodiments, some functions are implementedprimarily in hardware using, for example, a hardware state machine.Implementation of the hardware state machine so as to perform thefunctions described herein will be apparent to those skilled in therelevant arts.

Input-output controllers 230 could include any of a variety of knowndevices for accepting and processing information from a user, whether ahuman or a machine, whether local or remote. Such devices include, forexample, modem cards, network interface cards, sound cards, or othertypes of controllers for any of a variety of known input or outputdevices. In the illustrated embodiment, the functional elements ofserver 120 communicate with each other via system bus 204. Some of thesecommunications may be accomplished in alternative embodiments usingnetwork or other types of remote communications.

As will be evident to those skilled in the relevant art, LIMS serverapplication 280, as well as LIMS Objects 290 including LIMS servers 292and LIMS API's 294 (described below), if implemented in software, may beloaded into system memory 220 and/or memory storage device 225 throughone of input devices 202. LIMS server application 280 as loaded intosystem memory 220 is shown in FIG. 2 as LIMS server applicationexecutables 280A. Similarly, objects 290 are shown as LIMS serverexecutables 292A and LIMS API object type libraries 294A after they havebeen loaded into system memory 220. All or portions of these loadedelements may also reside in a read-only memory or similar device ofmemory storage device 225, such devices not requiring that the elementsfirst be loaded through input devices 202. It will be understood bythose skilled in the relevant art that any of the loaded elements, orportions of them, may be loaded by processor 205 in a known manner intosystem memory 220, or cache memory (not shown), or both, as advantageousfor execution.

LIMS Server Application 280: Details regarding the operations ofillustrative implementations of application 280 are provided in U.S.patent application Ser. No. 09/682,098 (hereby incorporated by referenceherein in its entirety for all purposes) and 60/220,587, incorporated byreference above. It will be understood that the particular LIMSimplementation described in this patent application is illustrativeonly, and that many other implementations may be used with LIMS objects290 and other aspects of the present or alternative embodiments.

Application 280, and other software applications referred to herein, maybe implemented using Microsoft Visual C++ or any of a variety of otherprogramming languages. For example, applications may also be written inJava, C++, Visual Basic, any other high-level or low-level programminglanguage, or any combination thereof.

As noted, certain implementations may be illustrated herein with respectto a particular, non-limiting, implementation of application 280,sometimes referred to as Affymetrix® LIMS. Full database functionalityis intended to provide a data streaming solution and a singleinfrastructure to manage information from probe array experiments.Application 280 provides all the functionality of database storage andretrieval system for accessing and manipulating all system data. Adatabase server provides an automated and integrated data managementenvironment for the end user. All process data, raw data and deriveddata are stored as elements of the database, providing an alternative toa file-based storage mechanism. A database back end also providesintegration of application 280 into a customer's overall informationsystem infrastructure. Data is accessible through standard interfacesand can be tracked, queried, archived, exported, imported andadministered.

Application 280 of the illustrated implementation, supports processtracking for a generic assay, adds enhanced administration functionalityfor managing GeneChip, spotted array, and AADM data (GeneChip data thathas been published to the Affymetrix® Analysis Data Model standard),provides a full Oracle® database management software or SQL Serversolution, supports publishing of genotype and sequence data, and providea high level of security for the LIMS system. Aspects of illustrativepublishing operations are described in U.S. patent application Ser. No.09/683,982, which is hereby incorporated herein in its entirety for allpurposes.

Application 280 of the illustrated example provides the followingfunctionality. The Generic assay, supported by process tracking fromenhancements to data management. The processes include but are notlimited to the following: sample definition, experiment setup,hybridization, scanning, grid alignment, cell intensity analysis, probearray analysis, and publishing. The generic assay supports multipleexperiments per sample definition via a re-queuing process, multiplehybridization and scan operations for a single experiment, datare-analysis, and publishing to more than one database. The ProcessDatabase, either an Oracle or SQL Server DBMS (Database managementsystem) solution, fully supported by enhancements to CasoAffy (COMCommunication layer to the process database). The GeneInfo Database,where enhancements provide additional support for storing chromosome andprobe sequence information about the biological item on the probe array.The AADM Database, a database that stores the published GeneChip data,where enhancements provide full support for either an Oracle or SQLserver DBMS. Additional tables to AADM provide support for genotypedata, and modifications to the publishing components include data loadperformance improvements as well as bi-directional communication withGeneChip during publishing operations. The Security Database, a LIMSsecurity database provides a role-based security level that isintegrated with the Windows NT® user authentication security. Thesecurity database supports role definition, functional access within arole and assigning NT groups and users to those roles. A role is acollection of users, which have a common set of access rights toGeneChip data. Roles are defined per server/database and a role membercan be a member of multiple roles, where the software determines auser's access rights. A function is a pre-determined action that iscommon to all roles. Each role is defined by the functions it can andcannot perform. Functions explicitly describe the type of action that amember of the role can perform. The functions supported by a newlycreated role includes but is not limited to the following: read processdata, delete process data, update process data, archive process data,assume ownership of process data, import, export process data, deleteAADM data, create a AADM database, and maintaining roles. When a newuser is added to a role they will have access privileges for their dataand read only access privilege for other user data within the same role.All non-role members are denied all access privileges to role member'sdata. When application 280 of the illustrated implementation isinstalled, at least two roles are created: administration and systemuser. The installer of the system software is added as a user to theadministration role and a selected Windows NT® group is added as a userto the system user role. The LIMS Manager, which is a stand-aloneapplication that provides user management capabilities for GeneChip®Analysis Suite data and AADM databases within the LIMS system. Thesecapabilities include but are not limited to the following: AADM databasecreation, publish data deletion, process data deletion, taking ownershipof process data, archiving and de-archiving of process data, dataexport, data import, role management, filter based find, managingexpression analysis parameter sets, and managing sample and experimentattribution templates.

The system supports high volume reference and research labs that wish tomanage and track laboratory workflow and GeneChip data, including DAT,EXP, CEL, CHP, CMP files that have been generated outside of the LIMSsystem, via a database. End users of the system include scientists,database administrators and system administrators.

LIMS Objects 290: LIMS Objects 290 is an optional object orientedprogrammers interface into LIMS server application 280. In theillustrated embodiment, LIMS objects 290 includes a number ofApplication Programmers Interfaces (APIs), generally and collectivelyrepresented as LIMS API's 294, and a number of LIMS servers, generallyand collectively represented as LIMS servers 292. LIMS servers 292 maybe distributed as out of process executables (“exe's”) and LIMS API's294 may be distributed as object type libraries (“tlb's”). It will beunderstood by those of ordinary skill in the art that various otherdistribution schemes and arrangements are possible.

LIMS Objects 290 typically may be used by an application developer(represented in FIG. 2 by applications developer 200) who wishes tointegrate in-house or third-party software systems with a LIMS such asLIMS server application 280. For example, it is illustratively assumedthat applications developer 200 works in an enterprise that employs LIMSserver application 280 to manage data related to experiments conductedon probe arrays, which may include any type of probe arrays 172. Itfurther is assumed for illustrative purposes that LIMS serverapplication 280 is not a full-service system in that it does not providefunctions such as laboratory process scheduling, sample management,instrument control, batch processing, and/or various data mining,processing, or visualization functions. Alternatively, application 280may provide some or all of these functions, but applications developer200 may wish to develop alternative or supplementary softwareapplications to perform all or portions of any of these or otherfunctions, and/or to integrate third-party software applications forthese purposes. LIMS objects 290 provides developer 200 with tools tocustomize both the input of data into, and output of data from, LIMSserver application 280.

LIMS objects 290 includes LIMS API's 294. API's 294, in a particularimplementation of LIMS COM API's, includes the classes of loading listof objects, reading an object, updating/writing an object, deleting anobject, processing data, creating AADM-compliant databases, andinvocation of the analysis controller. API's are also included forobjects, which are used by the previously listed classes.

Further aspects and implementations of the illustrated and otherembodiments include the AADM database schema, which can be divided intofour sub-schemas chip design, experiment setup, analysis results, andprotocol parameters. The chip design sub-schema contains the overallchip description including the name, number of rows and columns ofcells, the number of units, and a description of the units. Theexperiment setup sub-schema contains information on the chip used andthe target that was applied. The analysis results sub-schema stores theresults from any expression analysis. The protocol parameters sub-schemacontains parameter information relating to target preparation,experiment setup, and chip analysis. The AADM database can be queriedfor analysis results, protocol parameters, and experiment setup in asimilar fashion to the queries used by the Affymetrix® Data Mining Tool.The Affymetrix Data Mining Tool also uses a supplementary databasecalled the Data Mining Info database, which stores user preferences,saved queries, frequently asked queries, and probe set lists. The GeneInfo database, is used by Affymetrix® Microarray Suite, stores probe setinformation such as descriptions of probe sets, sequences that are tiledon an expression array, and user defined annotations. It also storeslists of external database links that allow users to add links tointernal/external databases, which could be public or private.

FIG. 3 is a functional block diagram that shows in greater detailillustrative components of a scanner system 100 that, as shown in FIG.1, may be coupled with LIMS server 120 via a network or otherwise. Asnoted, scanner system 100 includes a user computer 150 and scanner 170.

User Computer 150: User computer 150 may be a computing device speciallydesigned and configured to support and execute some or all of thefunctions of probe array applications 399, described below. Computer 150also may be any of a variety of types of general-purpose computers suchas a personal computer, network server, workstation, or other computerplatform now or later developed. Computer 150 typically includes knowncomponents such as a processor 305, an operating system 310, a graphicaluser interface (GUI) controller 315, a system memory 320, memory storagedevices 325, and input-output controllers 330. It will be understood bythose skilled in the relevant art that there are many possibleconfigurations of the components of computer 150 and that somecomponents that may typically be included in computer 150 are not shown,such as cache memory, a data backup unit, and many other devices.Processor 305 may be a commercially available processor such as aPentium® processor made by Intel Corporation, a SPARC® processor made bySun Microsystems, or it may be one of other processors that are or willbecome available. Processor 305 executes operating system 310, which maybe, for example, a Windows®-type operating system (such as Windows NT®4.0 with SP6a) from the Microsoft Corporation; a Unix® or Linux-typeoperating system available from many vendors; another or a futureoperating system; or some combination thereof. Operating system 310interfaces with firmware and hardware in a well-known manner, andfacilitates processor 305 in coordinating and executing the functions ofvarious computer programs that may be written in a variety ofprogramming languages. Operating system 310, typically in cooperationwith processor 305, coordinates and executes functions of the othercomponents of computer 150. Operating system 310 also providesscheduling, input-output control, file and data management, memorymanagement, and communication control and related services, all inaccordance with known techniques.

System memory 320 may be any of a variety of known or future memorystorage devices. Examples include any commonly available random accessmemory (RAM), magnetic medium such as a resident hard disk or tape, anoptical medium such as a read and write compact disc, or other memorystorage device. Memory storage device 325 may be any of a variety ofknown or future devices, including a compact disk drive, a tape drive, aremovable hard disk drive, or a diskette drive. Such types of memorystorage device 325 typically read from, and/or write to, a programstorage medium (not shown) such as, respectively, a compact disk,magnetic tape, removable hard disk, or floppy diskette. Any of theseprogram storage media, or others now in use or that may later bedeveloped, may be considered a computer program product. As will beappreciated, these program storage media typically store a computersoftware program and/or data. Computer software programs, also calledcomputer control logic, typically are stored in system memory 320 and/orthe program storage device used in conjunction with memory storagedevice 325.

In some embodiments, a computer program product is described comprisinga computer usable medium having control logic (computer softwareprogram, including program code) stored therein. The control logic, whenexecuted by processor 305, causes processor 305 to perform functionsdescribed herein. In other embodiments, some functions are implementedprimarily in hardware using, for example, a hardware state machine.Implementation of the hardware state machine so as to perform thefunctions described herein will be apparent to those skilled in therelevant arts.

Input-output controllers 330 could include any of a variety of knowndevices for accepting and processing information from a user, whether ahuman or a machine, whether local or remote. Such devices include, forexample, modem cards, network interface cards, sound cards, or othertypes of controllers for any of a variety of known input devices 302.Output controllers of input-output controllers 330 could includecontrollers for any of a variety of known display devices 380 forpresenting information to a user, whether a human or a machine, whetherlocal or remote. If one of display devices 380 provides visualinformation, this information typically may be logically and/orphysically organized as an array of picture elements, sometimes referredto as pixels. Graphical user interface (GUI) controller 315 may compriseany of a variety of known or future software programs for providinggraphical input and output interfaces between computer 150 and user 275,and for processing user inputs. In the illustrated embodiment, thefunctional elements of computer 150 communicate with each other viasystem bus 304. Some of these communications may be accomplished inalternative embodiments using network or other types of remotecommunications.

As will be evident to those skilled in the relevant art, applications399, if implemented in software, may be loaded into system memory 320and/or memory storage device 325 through one of input devices 302. Allor portions of applications 399 may also reside in a read-only memory orsimilar device of memory storage device 325, such devices not requiringthat applications 399 first be loaded through input devices 302. It willbe understood by those skilled in the relevant art that applications399, or portions of it, may be loaded by processor 305 in a known mannerinto system memory 320, or cache memory (not shown), or both, asadvantageous for execution.

Scanner 170: Scanner 170 of this example provides an image of hybridizedprobe-target pairs by detecting fluorescent, radioactive, or otheremissions; by detecting transmitted, reflected, or scattered radiation;by detecting electro-magnetic properties or characteristics; or by othertechniques. These processes or techniques may generally and collectivelybe referred to hereafter for convenience simply as involving thedetection of “emissions.” Various detection schemes are employeddepending on the type of emissions and other factors. A typical schemeemploys optical and other elements to provide excitation light and toselectively collect the emissions. Also generally included are variouslight-detector systems employing photodiodes, charge-coupled devices,photomultiplier tubes, or similar devices to register the collectedemissions. For example, a scanning system for use with a fluorescentlabel is described in U.S. Pat. No. 5,143,854, incorporated by referenceabove. Illustrative scanners or scanning systems that, in variousimplementations, may include scanner 170 are described in U.S. Pat. Nos.5,143,854, 5,578,832, 5,631,734, 5,834,758, 5,936,324, 5,981,956,6,025,601, 6,141,096, 6,185,030, 6,201,639, 6,218,803, and 6,252,236; inPCT Application PCT/US99/06097 (published as WO99/47964); in U.S. patentapplication Ser. Nos. 10/063,284, 09/683,216, 09/683,217, 09/683,219,09/681,819, and 09/383,986; and in U.S. Provisional Patent ApplicationSer. Nos. 60/364,731, and 60/286,578, each of which is herebyincorporated herein by reference in its entirety for all purposes.

Scanner 170 of this non-limiting example provides data representing theintensities (and possibly other characteristics, such as color) of thedetected emissions, as well as the locations on the substrate where theemissions were detected. The data typically are stored in a memorydevice, such as system memory 320 of user computer 150, in the form of adata file. One type of data file, such as image data 276 shown in FIG. 2that could for example be in the form of a “*.cel” file generated byMicroarray Suite software available from Affymetrix, Inc., typicallyincludes intensity and location information corresponding to elementalsub-areas of the scanned substrate. In the illustrated example of FIG.2, data 276 could be received by computer 150C where a *.cel file couldbe generated or the *.cel file could be generated by scanner 170.Alternatively data 276 may be directly processed or some other uses ofdata 276 known to those of ordinary skill in the related art. The term“elemental” in this context means that the intensities, and/or othercharacteristics, of the emissions from this area each are represented bya single value. When displayed as an image for viewing or processing,elemental picture elements, or pixels, often represent this information.Thus, for example, a pixel may have a single value representing theintensity of the elemental sub-area of the substrate from which theemissions were scanned. The pixel may also have another valuerepresenting another characteristic, such as color. For instance, ascanned elemental sub-area in which high-intensity emissions weredetected may be represented by a pixel having high luminance (hereafter,a “bright” pixel), and low-intensity emissions may be represented by apixel of low luminance (a “dim” pixel). Alternatively, the chromaticvalue of a pixel may be made to represent the intensity, color, or othercharacteristic of the detected emissions. Thus, an area ofhigh-intensity emission may be displayed as a red pixel and an area oflow-intensity emission as a blue pixel. As another example, detectedemissions of one wavelength at a particular sub-area of the substratemay be represented as a red pixel, and emissions of a second wavelengthdetected at another sub-area may be represented by an adjacent bluepixel. Many other display schemes are known. Various techniques may beapplied for identifying the data representing detected emissions andseparating them from background information. For example, U.S. Pat. No.6,090,555, and U.S. patent application Ser. No. 10/197,369, titled“System, Method, and Computer Program Product for Scanned ImageAlignment” filed Jul. 17, 2002, which are both hereby incorporated byreference herein in their entireties for all purposes, describe variousof these techniques. In a particular implementation, scanner 170 mayidentify one or more labeled targets. For instance, sample of a firsttarget may be labeled with a first dye (an example of what may moregenerally be referred to hereafter as an “emission label”) thatfluoresces at a particular characteristic frequency, or narrow band offrequencies, in response to an excitation source of a particularfrequency. A second target may be labeled with a second dye thatfluoresces at a different characteristic frequency. The excitationsource for the second dye may, but need not, have a different excitationfrequency than the source that excites the first dye, e.g., theexcitation sources could be the same, or different, lasers. The targetsamples may be mixed and applied to the probe arrays, and conditions maybe created conducive to hybridization reactions, all in accordance withknown techniques.

Probe-Array Analysis Applications 399: Generally, a human being mayinspect a printed or displayed image constructed from the data in animage file and may identify those cells that are bright or dim, or areotherwise identified by a pixel characteristic (such as color). However,it frequently is desirable to provide this information in an automated,quantifiable, and repeatable way that is compatible with various imageprocessing and/or analysis techniques. For example, the information maybe provided for processing by a computer application that associates thelocations where hybridized targets were detected with known locationswhere probes of known identities were synthesized or deposited. Othermethods include tagging individual synthesis or support substrates (suchas beads) using chemical, biological, electro-magnetic transducers ortransmitters, and other identifiers. Information such as the nucleotideor monomer sequence of target DNA or RNA may then be deduced. Techniquesfor making these deductions are described, for example, in U.S. Pat. No.5,733,729, which hereby is incorporated by reference in its entirety forall purposes, and in U.S. Pat. No. 5,837,832, noted and incorporatedabove.

A variety of computer software applications are commercially availablefor controlling scanners (and other instruments related to thehybridization process, such as hybridization chambers), and foracquiring and processing the image files provided by the scanners.Examples are the Jaguar™ application from Affymetrix, Inc., aspects ofwhich are described in PCT Application PCT/US 01/26390 and in U.S.patent application Ser. Nos. 09/681,819, 09/682,071, 09/682,074, and09/682,076, and the Microarray Suite application from Affymetrix,aspects of which are described in U.S. Provisional Patent ApplicationSer. Nos. 60/220,587, 60/220,645 and 60/312,906, all of which are herebyincorporated herein by reference in their entireties for all purposes.For example, image data in image data file 276 may be operated upon togenerate intermediate results such as so-called cell intensity files(*.cel) and chip files (*.chp), generated by Microarray Suite or spotfiles (*.spt) generated by Jaguar™ software. For convenience, the terms“file” or “data structure” may be used herein to refer to theorganization of data, or the data itself generated or used byexecutables 399A and executable counterparts of other applications.However, it will be understood that any of a variety of alternativetechniques known in the relevant art for storing, conveying, and/ormanipulating data may be employed, and that the terms “file” and “datastructure” therefore are to be interpreted broadly. In the illustrativecase in which image data file 276 is derived from a GeneChip® probearray, and in which Microarray Suite generates probe array intensitydata file 440, file 440 may contain, for each probe scanned by scanner170, a single value representative of the intensities of pixels measuredby scanner 170 for that probe. Thus, this value is a measure of theabundance of tagged cRNA's present in the target that hybridized to thecorresponding probe. Many such cRNA's may be present in each probe, as aprobe on a GeneChip® probe array may include, for example, millions ofoligonucleotides designed to detect the cRNA's. The resulting datastored in the chip file may include degrees of hybridization, absoluteand/or differential (over two or more experiments) expression, genotypecomparisons, detection of polymorphisms and mutations, and otheranalytical results. In another example, in which executables 399Aincludes image data from a spotted probe array, the resulting spot fileincludes the intensities of labeled targets that hybridized to probes inthe array. Further details regarding cell files, chip files, and spotfiles are provided in U.S. Provisional Patent Application Nos.60/220,645, 60/220,587, and 60/226,999, incorporated by reference above.

In the present example, in which executables 399A include Affymetrix®Microarray Suite, the chip file is derived from analysis of the cellfile combined in some cases with information derived from library files.A non-limiting example is illustrated in FIG. 4 as deviation data file(*.tab) 445 that specifies details regarding the sequences and locationsof probes and controls. Laboratory or experimental data may also beprovided to the software for inclusion in the chip file. For example, anexperimenter and/or automated data input devices or programs may providedata related to the design or conduct of experiments. As a non-limitingexample, the experimenter may specify an Affymetrix catalogue or customchip type (e.g., Human Genome U95Av2 chip) either by selecting from apredetermined list presented by Microarray Suite or by scanning a barcode related to a chip to read its type. Also, this information may beautomatically read. For example, a bar code (or other machine-readableinformation such as may be stored on a magnetic strip, in memory devicesof a radio transmitting module, or stored and read in accordance withany of a variety of other known techniques) may be affixed to the probearray, a cartridge, or other housing or substrate coupled to orotherwise associated with the array. The machine-readable informationmay automatically be read by a device (e.g., a 1-D or 2-D bar codereader) incorporated within the scanner, an autoloader associated withthe scanner, an autoloader movable between the scanner and otherinstruments, and so on. In any of these cases, Microarray Suite mayassociate the chip type, or other identifier, with various scanningparameters stored in data tables. The scanning parameters may include,for example, the area of the chip that is to be scanned, the startingplace for a scan, the location of chrome borders on the chip used forauto-focusing, the speed of the scan, a number of scan repetitions, thewavelength or intensity of laser light to be used in reading the chip,and so on. Rather than storing this data in data tables, some or all ofit may be included in the machine-readable information coupled orassociated with the probe arrays. Other experimental or laboratory datamay include, for example, the name of the experimenter, the dates onwhich various experiments were conducted, the equipment used, the typesof fluorescent dyes used as labels, protocols followed, and numerousother attributes of experiments.

As noted, executables 399A may apply some of this data in the generationof intermediate results. For example, information about the dyes may beincorporated into determinations of relative expression. Other data,such as the name of the experimenter, may be processed by executables399A or may simply be preserved and stored in files or other datastructures. Any of these data may be provided, for example over anetwork, to a laboratory information management server computer, such asLIMS server 120 of FIGS. 1 and 2, configured to manage information fromlarge numbers of experiments. A data analysis program may also generatevarious types of plots, graphs, tables, and other tabular and/orgraphical representations of analytical data. As will be appreciated bythose skilled in the relevant art, the preceding and followingdescriptions of files generated by executables 399A are exemplary only,and the data described, and other data, may be processed, combined,arranged, and/or presented in many other ways.

The processed image files produced by these applications often arefurther processed to extract additional data. In particular, data-miningsoftware applications often are used for supplemental identification andanalysis of biologically interesting patterns or degrees ofhybridization of probe sets. An example of a software application ofthis type is the Affymetrix® Data Mining Tool, described in U.S. patentapplication Ser. No. 09/683,980, which is hereby incorporated herein byreference in its entireties for all purposes. Software applications alsoare available for storing and managing the enormous amounts of data thatoften are generated by probe-array experiments and by theimage-processing and data-mining software noted above. An example ofthese data-management software applications is the Affymetrix®Laboratory Information Management System (LIMS). In addition, variousproprietary databases accessed by database management software, such asthe Affymetrix® EASI (Expression Analysis Sequence Information) databaseand database software, provide researchers with associations betweenprobe sets and gene or EST identifiers.

For convenience of reference, these types of computer softwareapplications (i.e., for acquiring and processing image files, datamining, data management, and various database and other applicationsrelated to probe-array analysis) are generally and collectivelyrepresented in FIG. 3 as probe-array analysis applications 399. FIG. 3illustratively shows applications 399 stored for execution (asexecutable code 399A corresponding to applications 399) in system memory320 of user computer 150.

As will be appreciated by those skilled in the relevant art, it is notnecessary that applications 399 be stored on and/or executed fromcomputer 150; rather, some or all of applications 399 may be stored onand/or executed from an applications server or other computer platformto which computer 150 is connected in a network. For example, it may beparticularly advantageous for applications involving the manipulation oflarge databases to be executed from a database server such as userdatabase server 120 of FIG. 1. Alternatively, LIMS, DMT, and/or otherapplications may be executed from computer 150, but some or all of thedatabases upon which those applications operate may be stored for commonaccess on server 120 (perhaps together with a database managementprogram, such as the Oracle® 8.0.5 database management system fromOracle Corporation). Such networked arrangements may be implemented inaccordance with known techniques using commercially available hardwareand software, such as those available for implementing a local-areanetwork or wide-area network. A local network is represented in FIG. 2by the connection of user computer 150 to user database server 120 (andto user-side Internet client 410, which may be the same computer) via anetwork cable, wireless network, or other means of networking known tothose in the related art. Similarly, scanner 170 (or multiple scanners)may be made available to a network of users over a network cable bothfor purposes of controlling scanner 170 and for receiving data inputfrom it.

In some implementations, it may be convenient for user 275 to groupprobe-set identifiers for batch transfer of information or to otherwiseanalyze or process groups of probe sets together. For example, asdescribed below, user 275 may wish to obtain annotation informationrelated to one or more probe sets identified by their respective probeset identifiers. Rather than obtaining this information serially, user275 may group probe sets together for batch processing. Various knowntechniques may be employed for associating probe set identifiers, ordata related to those identifiers, together. For instance, user 275 maygenerate a tab delimited *.txt file including a list of probe setidentifiers for batch processing. This file or another file or datastructure for providing a batch of data (hereafter referred to forconvenience simply as a “batch file”), may be any kind of list, text,data structure, or other collection of data in any format. The batchfile may also specify what kind of information user 275 wishes to obtainwith respect to all, or any combination of, the identified probe sets.In some implementations, user 275 may specify a name or otheruser-specified identifier to represent the group of probe-setidentifiers specified in the text file or otherwise specified by user275. This user-specified identifier may be stored by one of executables399A, so that user 275 may employ it in future operations rather thanproviding the associated probe-set identifiers in a text file or otherformat. Thus, for example, user 275 may formulate one or more queriesassociated with a particular user-specified identifier, resulting in abatch transfer of information from portal 400 to user 275 related to theprobe-set identifiers that user 275 has associated with theuser-specified identifier. Alternatively, user 275 may initiate a batchtransfer by providing the text file of probe-set identifiers. In any ofthese cases, user 275 may provide information, such as laboratory orexperimental information, related to a number of probe sets by a batchoperation rather than serial ones. The probe sets may be grouped byexperiments, by similarity of probe sets (e.g., probe sets representinggenes having similar annotations, such as related to transcriptionregulation), or any other type of grouping. For example, user 275 mayassign a user-specified identifier (e.g., “experiments of January 1”) toa series of experiments and submit probe-set identifiers inuser-selected categories (e.g., identifying probe sets that wereup-regulated by a specified amount) and provide the experimentalinformation to portal 400 for data storage and/or analysis.

Genotype and Statistical Analysis Manager 400: FIG. 4 is a functionalblock diagram of a particular implementation of probe-array analysisapplications executables 399, referred to as executables 399A, thatincludes genotype and statistical analysis manager 400. As noted,manager 400 provides a user with information related to results fromexperiments with probe arrays. In the illustrated implementation,manager 400 includes input manager 405, genotype analysis determiner410, statistical analysis determiner 425, genotype comparator 420, andoutput manager 430. The function and purpose of each component will bedescribed in detail below. It should be understood that the functions ofthese elements may be distributed and/or combined among them in numerousvariations and that not all functions need be present in alternativeimplementations. For example, the functions of input manager 405 and/oroutput manager 430 could be performed by one or both of comparator 420or determiner 410. Similarly, the functions of determiner 410 andcomparator 420 could be performed by either alone in otherimplementations, or otherwise distributed. Thus, generally, functionsare assigned to the elements described below for purposes of claritywith respect to the illustrated implementation, but these descriptionsshould be understood to be to be non-limiting.

One function of input manager 405 in the illustrated implementation isto receive one or more sets of data from probe array data files 323 andprovide the one or more sets of data to the appropriate elements ofmanager 400. The data could include probe array intensity data file(e.g., *.cel) 440 that could include image data 276, deviation data file(e.g., *.tab) 445, or other types of data that could include variouslibrary or experiment files. Another function of the illustratedimplementation of manager 405 is to determine where to direct the one ormore data files that may include multiple data files of the same type.For example, two probe array intensity data files 440 may be processedby input manager 405 for the purpose of determining differences ingenotype calls. Manager 405 may direct data from both files, as well asadditional library or experiment files as appropriate, to genotypeanalysis determiner 410 (discussed further below).

Additionally, input manager 405 may distinguish between files thatcorrespond to different types of probe arrays but may be of the samedata file type, e.g., instances of data file 440 from experiments withdifferent types of probe arrays. Manager 405 may determine the probearray type from analysis of the intensity data file by, for example,comparing features of the file to a template or look-up table offiducial features. Probe array types could include those designed forgenotype analysis, expression analysis, or other type of analysis.Alternatively, manager 405 could identify the probe array types byconsulting additional data files including experiment files, libraryfiles, or some other means of identification in accordance withtechniques known to those of ordinary skill in the related art. In aparticular implementation, input manager 405 receives first emissionintensity data and second emission intensity data corresponding toprobes disposed upon a biological probe array and directs this data togenotype analysis determiner 410 and/or statistical analysis determiner425 for processing as described below.

Also illustrated in FIG. 4 is genotype analysis determiner 410 that, insome implementations, determines first and second genotype calls for oneor more probe sets based, at least in part, on the first and secondemission intensity data provided by input manager 405. Thus, determiner410 analyzes results from probe arrays designed specifically tointerrogate particular regions of a genome that may include what arereferred to as a single nucleotide polymorphisms (hereafter referred toas SNP's), or other features known to those of ordinary skill in therelevant art that could be used for genotyping. Generally speaking, aSNP is a single base pair difference within a DNA sequence that isdifferent from what is the most commonly identified base in a sequencefrom a population. A SNP is commonly defined to occur at a frequencythat is greater than one percent of the population. An occurrence ofless than one percent is commonly referred to as a mutation, thus a SNPis a more common event in a population than a mutation. The term“population” as used here commonly refers to the general knownpopulation, although it could be used to refer to smaller groups thatmay be separated by ethnicity, geographical location, or some otherdistinction. Each probe set on the probe array may, for example, bedesigned to interrogate a particular “biallelic” SNP. The term“biallelic” refers to a state where there are two possible bases forthat SNP (what are commonly referred to as the consensus and thepolymorphic base) and each is referred to as an allele, typicallyrepresented as the A allele and the B allele. As an alternative example,the probe array could be designed to interrogate “multiallelic”sequences that could be related to what are referred to by those in theart as microsattelites, or other situations where more than two possiblealleles exist. The term “interrogate” is used broadly to mean that theprobe set is designed to determine which of alternative nucleotides ispresent in a particular sample, e.g., to distinguish a “G” from a “C” ina particular position.

Generally each probe set is designed to interrogate a different SNP,although an exception is the case in which two probe sets are designedto interrogate both the coding strand of DNA, known as the sense strand,and the complementary non-coding strand, known as the anti-sense strand,for the same SNP. A probe set may also be referred to as a BLOCK ofprobes where, for example, two or more probes within the BLOCK mayinterrogate the same DNA sequence except for the SNP base position. Forexample a pair of probes may be designed to interrogate the A and Balleles of the SNP respectively and may be referred to for convenienceof reference as a miniBLOCK.

A BLOCK may be comprised of a plurality of miniBLOCKS that each aredesigned to interrogate the same SNP but may differ in the exactsequence to be interrogated. For instance, one miniBLOCK may interrogatethe SNP position at the centermost position of the probe sequence, and adifferent miniBLOCK may interrogate the SNP position at one of the endsof the probe sequence. The result is that the probe sequences may differfrom one another slightly between miniBLOCKS. As a further example, theminiBLOCK could consist of four probes where two probes are designed tointerrogate the A allele and two for the B allele. In the presentexample, one of the probes from the A allele pair and one from the Ballele pair may interrogate a perfect match to the desired DNA sequence.The other may be designed to interrogate a mismatch that could be asimilar sequence to the perfect match probe with one or more base pairdifferences at one or more different positions in the probe. Thecombination of the perfect match and the mismatch probes in addition tothe number and sequence composition of miniBLOCKS could further be usedto determine the hybridization efficiency or some other experimentalaspect that may increase the accuracy of genotype calls. Additionalexamples of genotyping probe arrays are described in PCT Application No.WO 95/11995, which is hereby incorporated by reference herein in itsentirety for all purposes.

In an illustrated implementation, file 440 contains emission intensityvalues corresponding to each probe of every probe set disposed upon asingle probe array. As noted above with respect to an illustrativeimplementation, the intensity values generally represent the degree ofhybridization, or not, of a probe with a labeled target. Determiner 410analyzes the emission intensity value for each probe of a probe set andmakes a call for the probe set where the call may include a genotypedetermination. The genotype determination call may include assigning aquantitative representation of the intensity values for the probe set,referred to as the relative allele signal (hereafter referred to forconvenience as the “RAS”). The value of the RAS, for example, maycorrespond to the allele of the base located at the SNP position and onthat basis may be assigned a qualitative genotype call as either A, B,or AB. In the present example, the call may thus be indicative of eithera homozygous or heterozygous condition, as will be discussed furtherbelow in relation to comparator 420.

In the illustrated implementation determiner 410 generates analysisinformation including, but not limited to, probe array data, RAS datafor each probe set, and a qualitative call for each probe set. Thisinformation, as processed for formatting or other purposes in thisimplementation by output manager 430, may be stored in analysis outputfile 450. As non-limiting examples, output manager 430 may also processthis information for storage in one or more databases, presented to theuser within a GUI, and/or directed to genotype comparator 420. Examplesof data stored in output file 450 are described later in reference tooutput manager 430.

As noted, manager 400 of the illustrated implementation also includesgenotype comparator 420 that compares first genotype calls withcorresponding second genotype calls (e.g., where the calls are made bydeterminer 410) and with a reference value. For example, comparator 420in some implementations identifies probe sets in which a differentgenotype call has been made between two experimental conditions thatcould relate to what is referred to by those of ordinary skill in therelevant art as loss of heterozygosity. Loss of heterozygosity is acharacteristic associated with several types of cancer where a normaltissue may be heterozygous in specific genes and a cancer tissue may behomozygous in the same genes. A gene typically exists as two copies(there are cases where more than two copies exist). Typically there isone copy of a gene on each chromosome of a pair (chromosomes typicallyoccur in pairs in eukaryotes), but the copies are not always exactly thesame in which case each unique copy is referred to as an allele. Anallele may function normally, or the allele may either lose or gain afunction with the consequence that cell processes are disrupted andpotentially detrimental effects ensue. As is known to those of ordinaryskill in the relevant art, if both copies have the same allele the geneis in a homozygous state, e.g., represented as “AA” or “BB.”Alternatively, if there are two different alleles, the gene is in aheterozygous state, e.g., represented as “AB.” In cases in which aparticular allele functions abnormally, there may be little or no effectif it is paired with a normal functioning allele in the heterozygousstate. But if there a two alleles with abnormal function in thehomozygous state, there could be deleterious effects. It is alsopossible to have two normally functioning alleles in a homozygous state,so a homozygous state is not necessarily a sign of deleterious effects.

In the illustrated implementation, genotype comparator 420 may receiveinformation from a plurality of output files 450 that correspond to thesame probe array type. Comparator 420 may also receive information fromone or more library files from probe array data files 323 or othersource that could include input manager 405. For example, a library filecould include deviation data file 445 that contains experimentallyderived standard deviation values for each probe set of the probe arraytype corresponding to files 450.

Comparator 420 compares the genotype call results from determiner 410for each probe set on a first probe array against the results for thecorresponding probe set on one or more second probe arrays. For example,comparator 420 may receive information from two output files 450 filesthat could be the results from scanned HuSNP™ probe arrays fromAffymetrix, Inc. The probe arrays are identical in probe set compositionand order, but have been exposed to two different experimental samples.File 445 in this example includes standard deviation values for each ofthe probe sets on the probe array to address experimental differencesthat may be unique to each probe set. In the present example, analysisoutput files 450 may have been created at the same time or at times thatmay differ by large time periods. For instance a file 450 may have beencreated from a tissue sample from an area of skin at one time, and thesecond may have been created years later from the same area of skin. Forinstance the second sample could be from an area that may be presumed tohave developed skin cancer where the two files could then be directlycompared so that a potentially detrimental loss of heterozygosity in oneor more critical genes could be identified.

Returning to the present implementation, the quantitative genotype callvalues are compared between the two files, along with a reference valuefrom the standard deviation file, to generate a quantitative value forthe change in RAS, referred to hereafter as normalized delta RAS. Thestandard deviation file contains reference values that arerepresentative of the variation that is specific to each probe set. Thereference values may be experimentally derived from one or more sets ofdata or by some other method where a specific value may be applied toeach probe set independently. The reference values could also bemodified or completely replaced by user selected values. The referencevalue may be used to normalize the value of delta RAS that correspondsto the same probe set. The term “normalize” as used herein refers to amathematical or other process to account for variation between samplesthat in this case may apply to variation between intensity values ofprobe sets. Variations could be caused by factors such as the influenceof flanking sequences, and numerous other sources known to those ofordinary skill in the relevant art. Examples of normalized delta RAS areillustrated in FIG. 5A as normalized relative allele signal difference510. The column associated with signal difference 510 has quantitativevalues for normalized delta RAS related with the probe set associatedwith each row. In some cases a probe set may not have a normalizedrelative allele difference 510 value associated with it. This could bethe result of a variety of factors including the lack of a referencevalue to be used for normalization, some user selectable parameter, orother reason for the omission of a significant value. If no value isassociated with difference 510, a value of “−1000” as shown in thisexample, or any other value or symbol in other implementations, may bedisplayed in the column. Also in the present implementation, there maybe a column to display non-normalized RAS in addition to or instead ofthe normalized RAS column. An example of the non-normalized RAS columnis illustrated in FIG. 5A as relative allele signal difference 512.

The value of delta RAS may be calculated by a variety of methods. Onemethod includes implementing the following equation:Delta RAS=|RAS sample 1−RAS sample 2|

Delta RAS represents the absolute value of the difference between theRAS from the first sample and the RAS from the second sample. The term“absolute value” as used herein may be the distance from zero or apositive reference value. The absolute value of the difference willyield a non-negative number, so if the RAS from sample 2 is a largernumber than the RAS from sample 1 then the delta RAS will still be apositive number representing the degree of difference between the twovalues.

In the present example the reference standard deviation value may beincorporated into the calculations with the following equation:Normalized Delta RAS=(Delta RAS)/(Probe Set Standard Deviation)

Delta RAS could represent change in either direction such as a loss ofheterozygosity (i.e. going from an AB genotype to AA or BB), or a gainof heterozygosity (i.e. going from AA or BB to AB). This information maybe presented to a user using an interface such as illustrative GUI 382Aof FIG. 5A. For instance, loss of heterozygosity example 550 involvingprobe set WIAF-3542 is identified in the column labeled as probe setidentifier 520. Example 550 has a qualitative genotype call of AB andcorresponding quantitative RAS of 0.276 in first experimental sample553. The genotype call changes to an A genotype call (an abbreviation ofa homozygous AA genotype call) in second experimental sample 555 and hasan associated RAS value of 0.749. The change between sample 553 andsample 555 is represented by quantitative difference 557 in the relativeallele signal difference 512 column having an associated value of 0.473,and a normalized difference 558 of 12.785. The values for normalizedrelative allele signal difference 510 and relative allele signaldifference 512 may represent a shift in signal from a heterozygous calltowards a homozygous call of either of the alleles, or alternativelyfrom a homozygous call of either of the alleles towards a heterozygouscall. Difference 510 and 512 may also represent cases in which there hasbeen no change in the genotype calls. The shift in genotype call may bebased on a numerical threshold value applied to the difference 510. Forinstance the threshold value in the present example could be 0.2, wherethe value of 0.516 illustrated by difference 557 is above the 0.2threshold value and thus confirms that the genotype call has changed.Values below the threshold value may be disregarded as experimentalvariation and called as no change in genotype.

Relative allele signal 535 may be comprised of RAS1 and RAS2 asillustrated in FIG. 5A, where RAS1 may correspond to a probe set thatinterrogates the coding strand of DNA. RAS2 may be a probe set designedto interrogate the non-coding strand, an additional copy of the samegene that may include one or more different alleles, or other sequencethat may assist in the determination of the genotype of the particulargene represented by RAS1. As illustrated in FIG. 5A, there may notalways be a probe set that corresponds to RAS2 and entries in thiscolumn may therefore be assigned a value of −1000 or other value,indicator, or symbol that indicates that absence of a correspondingprobe set. Alternatively when RAS 2 has an associated probe set, RAS2may have a value similar to that of RAS1 that represents a genotype callfor the interrogated sequence. An algorithm may be used that includesboth RAS1 and RAS2 for the determination of a value in the qualitativegenotype call 530 column. For example, the algorithm could include theaverage RAS value for RAS1 and RAS2. The equation for the calculationmay include:RAS sample=(RAS1+RAS2)/2

The algorithm could also weight one of the values higher than the other.For instance RAS1 may be weighted more heavily than RAS2 because itcorresponds to the coding strand of DNA. Also, the use of the referencevalues may be different when there are two probe sets RAS1 and RAS2. Forexample, the following equation could be used in the calculation fornormalized delta RAS:Normalized Delta RAS=(Delta RAS)/(√{square root over ((Std. dev1)²+(Std. dev 2)²)}{square root over ((Std. dev 1)²+(Std. dev 2)²)})

Where Std. dev 1 is the standard deviation value for the first probeset, and Std. dev 2 is the standard deviation value for the second probeset. The standard deviation values in this example may account forvariability between probe sets that could be caused by factors such asdifferences in the sequence composition of the DNA sequence thatneighbors the probe set target sequence.

It will be understood that the preceding equations and algorithms areillustrative only, and that other statistical representations known tothose of ordinary skill in the relevant art may be used in alternativeimplementations. Further examples of genotyping methods using relativeallele signals are described in U.S. patent application Ser. No.09/758,872 that is hereby incorporated by reference herein in itsentirety for all purposes.

In the illustrated implementation, comparator 420 generates informationthat is stored by output manager 430 in report data file 455. File 455contains the delta RAS results for each probe set along with other probeset related information that could include data from probe arrayintensity data file 440 and analysis output file 450. Alternatively,comparator 420 may filter the data included in data file 455, based onone or more parameters provided by user 275 and as discussed further inrelation to graphical user interface 382B of FIG. 5B.

Output manager 430 may, in some implementations, display a measure ofnormalized change between the first and second genotype calls based, atleast in part, on a comparison of first and second genotype calls andreference value. More specifically, with respect to an illustrativeimplementation, comparator 420 directs report data file 455 to outputmanager 430 where it may be stored in one or more databases such asprobe array data files 323 and/or displayed to user 275 via a graphicaluser interface. Report data file 455 may also be compared to data filesfrom one or more databases to correlate the changes in genotype callswith other specific signatures that may relate to a potential disease.For example, some loss of heterozygosity in some genes may have nodetrimental effect, while in others the effect could be significant.Also, particular combinations of genes that have demonstrated a loss ofheterozygosity could demonstrate the existence of, or a predispositionto, a disease condition such as cancer. The report file may be used tomake a comparison against databases with disease data profiles andreport back a diagnostic quantitative and/or qualitative call. Suchdatabase comparisons could be at the level of probe set, or could be acollective comparison of genotype calls (e.g., a haplotype analysis)that may be used, among other things, for population-based associationstudies.

In addition to the implementations involving genotype analysis describedabove, manager 400 in some embodiments includes functional elements forproviding statistics-based expression analysis. For example, manager 400may include statistical analysis determiner 425 that determines absoluteor relative expression values based, at least in part, on a statisticalmeasure of the emission intensity data and at least one user-selectablestatistical parameter. In these implementations, output manager displaysthe absolute or relative expression values based, at least in part, onat least one user-selectable display parameter. For example, statisticalanalysis determiner 425 may use specific statistical algorithms designedto analyze emission intensity values from files derived from scannedprobe arrays that test the expression of mRNA in an experimental sample,such as *.cel files from Affymetrix® GeneChip® probe arrays designed forexpression analysis. Determiner 425 may perform a single-file analysisthat evaluates the emission intensity values for each probe of a probeset on a single probe array and generates a detection p-value. Thep-values for each probe of a probe set are further evaluated to make adetection call that corresponds to an mRNA transcript and that includesa present, absent, or no call. For example, a p-value close to zero mayin some implementations be called as transcript present, whereas ap-value near 1 would be called as transcript absent.

Determiner 425 may also perform multiple-file analysis in order todetermine the change of expression level of mRNA transcripts. In such ananalysis, a p-value is generated that may be evaluated to make a changeof expression call that, for example, could include an increase,decrease, or no change call. As used in this context, the term “p-value”refers to a measure of likelihood of a change of direction. For example,p-values close to 0.0 may indicate a high likelihood for an increase,values near 1.0 may indicate a high likelihood for a decrease, andvalues near 0.5 may indicate a weak likelihood for change in eitherdirection.

Determiner 425 may receive user-selected parameters directly frominput-output controllers 330 or as part of a data file from inputmanager 405. The user-selected parameters could be used in an algorithm,such as the One-Sided Wilcoxon's Signed Rank Test, for the calculationof the p-value to increase or decrease the sensitivity and/orspecificity of the p-value. It will be understood that this test is anon-limiting example, and that other statistical tests or measures maybe used in other implementations. For example, the user may choose tochange a threshold value based on observed or calculated experimentalvariation or other criterion. More specifically in the present exampleof the p-value ranges noted above, the user may increase a thresholdvalue above a small positive number such as 0.015. If raised, thethreshold number may reduce the number of false present calls, but couldalso reduce the number of true present calls. Examples of statisticaltests and algorithms are described further in U.S. patent applicationSer. No. 09/735,574, which is hereby incorporated by reference herein inits entirety for all purposes.

Other user-selected parameters could include those used for theevaluation of the p-value in order to make a detection, change, or othertype of call. For example, for a p-value that has a range between 0 and1, the boundaries between calls could be 0.4 and 0.6 where p-valuesbelow 0.4 could be called as present, between 0.4 and 0.6 could becalled as no call, and higher than 0.6 could be called as absent. In thepresent example, the user-selectable parameters could include theboundaries, in which case adjusting the values higher or lower couldaffect the sensitivity and/or specificity of the call. Furtherdescriptions of the statistical algorithms and associated calls aredescribed in U.S. patent application Ser. No. 09/758,872, which ishereby incorporated by reference herein in its entirety for allpurposes.

In the illustrated implementation, output manager 430 receives datafiles from a plurality of sources such as genotype analysis determiner410, genotype comparator 420, or statistical analysis determiner 425,and may save them in one or more databases that could be local orremote. For example, manager 430 may save a data file directly intoprobe array data files 323 or some other local location in addition toor instead of data files 323. Also, manger 430 may direct data files toremote databases through input-output manager 330. The remote databasesmay be located on LIMS server 120 connected by network 125 or some otherremote database connected by the same or another network or by othermethods known to those of ordinary skill in the art.

Output manager 430 may also direct data files to display/output devices380 via input-output controllers 330 where the data files may beconverted to GUI's 382 to be displayed to user 275. Illustrativeexamples of GUI's 382 from converted data files are presented in FIGS.5A, 5B, and 6 as graphical user interfaces 382A, 382B, and 382C. GUI's382 are comprised of graphical elements that in this illustrative andimplementation that are constructed such that the data is presented invertical columns and/or horizontal rows. More generally, GUI's 382 couldbe comprised of any graphical, text, or other format constructed toillustrate associations, relationships, and/or differences with color,columns/rows, or other method in accordance with known techniques orthose that may be developed in the future. For example, illustrated inFIG. 5A is probe set identifier 520 in which a list of probe sets isarranged in a vertical column. The data that corresponds to anindividual probe set is arranged in a horizontal row such as isillustrated as loss of heterozygosity probe set 550. In the presentexample, the probe set identifier is located on the far left. Along therow to the right there are columns that include SNP location data 525,genotype calls 530 and relative allele signals 535 for each experimentalsample, and relative allele signal difference 510. The columns in eachrow contain data that correspond to each probe set listed in that row.Another element of interface 382A has a number includes, but is notlimited to, probe array intensity data file information 540 that mayprovide information from one or more sets of data that could includeprobe array intensity data 540. For example, SNP location data 525displays to the user the chromosome number and the estimated geneticdistance from the SNP to the top of the short arm of the chromosome. Theestimated genetic distance may be expressed as the number of bases, orin a preferred implementation in centimorgans. As is known by those ofordinary skill in the relevant art, a centimorgan may be defined as aunit of measure of recombination frequency. One centimorgan is equal toa 1% chance that a marker at one genetic locus will be separated from amarker at a second locus due to crossing over in a single generation.For example, in human beings, 1 centimorgan is equivalent, on average,to 1 million base pairs. Data 525 could also display relative distancesto the next closest SNP, centromere, or other feature located on achromosome.

Output manager 430 may also perform output and results filtering in someimplementations. For example, manager 430 may receive user inputs toselect specific parameters for sorting or displaying specificinformation based at least in part upon the input parameters. Some ofthe filtering operations or other processes that include user selectedparameters could also be performed by comparator 420 or determiner 425.

FIG. 5B illustrates an additional example where the user may filter theresults according to specific parameters. Graphical user interface 382Bis an example in which the user has selected to filter the results basedon a change of relative allele signal difference 512 column as indicatedby the differences in qualitative genotype call 530 where the callchanges from a heterozygous call to a homozygous call for either alleletype. Only those probe sets and the corresponding data in the same rowthat satisfy the criteria are a part of the filtered data set. Also theuser may select which columns to view or delete from view, for examplegraphical user interface 382B of FIG. 5B illustrates a limited number ofcolumns where the user may have designated the number and or compositionof the columns.

The user-selected criteria may be received by comparator 420 frominput-output controllers 330 so that the user may dynamically inputdifferent criteria in response to viewing interface 382B. Alternatively,the user-selected criteria could be included in deviation data file 445or other data file that could include library or experiment data filesthat are directed to comparator 420. In the present example, comparator420 (typically, as with determiner 410, by passing information to outputmanager 430) creates analysis output file 450 that contains only thefiltered data. One benefit is that the size of the data file may bereduced so that data management is simplified with only data that isrelevant to the user's experimental needs.

The filtering of data that results in a graphical user interface such asinterface 382B of FIG. 5B could also be performed by output manager 430on an unfiltered data file. For example, manager 430 may receiveinformation needed to generate report data file 455 from comparator 420and the user-selected parameters from input-output controllers 330 inorder to perform the filtering operation. Manager 430 filters the datawithin data file 455 based upon the user-selected parameters anddisplays the filtered results in interface 382B. Manager 430 couldcreate a new data file to store the filtered results in one or more datastructures that could be the same or different file type as data file455. In the present example, original data file 455 may remain unchangedwhere the user may recall data file 455 in order to filter by otherparameters, display all of the data contained in the unfiltered datafile, or use for the purposes of comparison with one or more other datafiles. Any of numerous conventional techniques may be employed toimplement these and other filtering operations.

FIG. 6 is yet another illustrative example of a GUI for the display ofanalysis results in which graphical user interface 382C is a display ofone possible result from statistical analysis determiner 425. Graphicaluser interface 382C is an example of the results from a detection callalgorithm where the detection call includes a qualitative andquantitative value for each probe disposed upon a single probe array.Detection call 620 is a column where the qualitative detection callresults may be displayed. The call may be based at least in part uponthe quantitative value shown as P-value 630 that, in turn, may be basedat least in part upon emission intensity data 625. In the presentexample the call could include “present,” “absent” or “no call”depending at least in part upon the p-value that is representative ofthe level of detected expression for each mRNA molecule corresponding toa particular probe set. GUI 382C, and numerous variations thereof, mayalso be used to display other results from determiner 425, such as thedetection of the change of expression when comparing the probe sets fromtwo experiments that tested different sample on the same probe arraytype. Similarly, the results displayed in the call 620 column are basedupon p-value 630. As with respect to GUI's 382A and 382B, therelationships indicated in GUI 382C using geometric arrangement (e.g.,rows and columns in this example) could be implemented in numerous otherways in other implementations. For example, graphical elementsindicating detection call and/or p value could be associated with probeset identifiers by using graphical plots in one, two, or threedimensions (e.g., scatter plots, histograms, and many otherarrangements); arrows, edges, or other graphical indicators ofrelationships; or commonality of qualities such as color, shade,intensity, and so on. Thus, GUI's 382 should be considered asnon-limiting examples of techniques for indicating relationships amongvalues, and/or the values themselves, in an interface accessible by auser. Audio, video, and other media, in various combinations or alone,may also be used to indicate these relationships and/or values to auser.

Further aspects of processing probe array data to generate genotypingcalls and measurements are described in a U.S. patent application Ser.No. 10/219,503, titled “System, Method, and Computer Software forGenotyping Analysis and Identification of Allelic Imbalance,” filedconcurrently herewith and hereby incorporated by reference herein in itsentirety for all purposes.

Having described various embodiments and implementations, it should beapparent to those skilled in the relevant art that the foregoing isillustrative only and not limiting, having been presented by way ofexample only. Many other schemes for distributing functions among thevarious functional elements of the illustrated embodiment are possible.The functions of any element may be carried out in various ways inalternative embodiments. For example, some or all of the functionsdescribed as being carried out by determiner 410 could be carried out bycomparator 430, or these functions could otherwise be distributed amongother functional elements. Also, the functions of several elements may,in alternative embodiments, be carried out by fewer, or a single,element. For example, the functions of determiner 410 and comparator 420could be carried out by a single element in other implementations.Similarly, in some embodiments, any functional element may performfewer, or different, operations than those described with respect to theillustrated embodiment. Also, functional elements shown as distinct forpurposes of illustration may be incorporated within other functionalelements in a particular implementation. For example, the functionsperformed by the two computers could be performed by a single server orother computing platform, distributed over more than two computerplatforms, or other otherwise distributed in accordance with variousknown computing techniques.

Also, the sequencing of functions or portions of functions generally maybe altered. Certain functional elements, files, data structures, and soon, may be described in the illustrated embodiments as located in systemmemory of a particular computer. In other embodiments, however, they maybe located on, or distributed across, computer systems or otherplatforms that are co-located and/or remote from each other. Forexample, any one or more of data files or data structures described asco-located on and “local” to a server or other computer may be locatedin a computer system or systems remote from the server. In addition, itwill be understood by those skilled in the relevant art that control anddata flows between and among functional elements and various datastructures may vary in many ways from the control and data flowsdescribed above or in documents incorporated by reference herein. Moreparticularly, intermediary functional elements may direct control ordata flows, and the functions of various elements may be combined,divided, or otherwise rearranged to allow parallel processing or forother reasons. Also, intermediate data structures or files may be usedand various described data structures or files may be combined orotherwise arranged. It further will be understood that references hereinto such terms as “file,” “data structure,” or “database” areillustrative only and that, in various implementations, data describedas being stored in a “file” may alternatively be stored in a database orotherwise stored in accordance with techniques and conventions familiarto those of ordinary skill in the relevant art or in accordance withtechniques that may be developed in the future. Data stored in files,databases, or other structures or in accordance with other techniquesmay be stored locally and/or may be stored remotely, e.g., data may bedistributed over a number of local and/or remote files or databases.Databases may be flat, relational, object oriented, or structured inaccordance with other techniques known to those of ordinary skill in therelevant art or that may be developed in the future. Numerous otherembodiments, and modifications thereof, are contemplated as fallingwithin the scope of the present invention as defined by appended claimsand equivalents thereto.

What is claimed is:
 1. A method of determining at least one genotypecall for at least two nucleic acid samples, the method comprising:inputting at least two sets of hybridization intensity data into acomputer, wherein the computer comprises a computer program designed toreceive sets of hybridization intensity values, and wherein the sets ofhybridization intensity values are based upon emission intensity dataobtained by hybridizing a nucleic acid sample with a nucleic acid probearray; assigning, with the computer program, quantitativerepresentations to at least a portion of each set of hybridizationintensity values, wherein the quantitative representations are eachbased upon one or more hybridization intensity values; calling, with thecomputer program, at least one genotype call for each nucleic acidsample, wherein the at least one genotype call is based upon thequantitative representations; calculating, with the computer program, aquantitative difference between the quantitative representations of atleast two nucleic acid samples; and determining, with the computerprogram, whether the at least one genotype call is different between theat least two nucleic acid samples, wherein determining comprisesanalysis of the quantitative difference between the quantitativerepresentations upon which the at least one genotype call was based. 2.The method of claim 1, wherein at least one quantitative representationis based upon a plurality of hybridization intensity values associatedwith a nucleic acid probe set of the nucleic acid probe array.
 3. Themethod of claim 1, wherein at least one quantitative representation isbased upon two or more nucleic acid probe sets of the nucleic acid probearray.
 4. The method of claim 3, wherein the quantitativerepresentations based upon two or more nucleic acid probe sets are anaverage of the hybridization intensity values of the two or more nucleicacid probe sets.
 5. The method of claim 3, wherein the quantitativerepresentations based upon two or more nucleic acid probe sets weighmore heavily the hybridization intensity values of at least one of thetwo or more nucleic acid probe sets.
 6. The method of claim 1, whereinthe quantitative difference is normalized.
 7. The method of claim 6,wherein the quantitative difference is normalized with respect to areference value.
 8. The method of claim 7, wherein the reference valueis experimentally derived.
 9. The method of claim 7, wherein thereference value is based upon at least one nucleic acid probe upon whichthe difference is based.
 10. The method of claim 7, wherein thereference value is based upon a standard deviation value associated withat least one nucleic acid probe upon which the difference is based. 11.The method of claim 7, wherein the reference value is modified, at leastin part, by a user selected value.
 12. The method of claim 1, whereindetermining whether the at least one genotype call is different betweenthe at least two nucleic acid samples additionally comprisesascertaining whether there was a loss or a gain of heterozygosity withinthe at least two nucleic acid samples.
 13. The method of claim 1,wherein the analysis of the quantitative difference includes comparingthe quantitative difference to a threshold value, wherein the at leastone genotype call is determined to be different between the at least twonucleic acid samples only if the quantitative difference is equal to orlarger than the threshold value.
 14. The method of claim 13, wherein thequantitative difference is normalized before analysis.
 15. The method ofclaim 1, additionally comprising: assessing a copy number state for atleast one nucleic acid sample based upon, at least in part, the at leastone genotype call.
 16. The method of claim 1, additionally comprising:diagnosing a disease condition based upon, at least in part, thequantitative representations.
 17. The method of claim 16, whereindiagnosing the disease condition is additionally based upon one or moredisease data profiles.
 18. The method of claim 17, wherein diagnosingthe disease condition is based upon a plurality of genotype calls. 19.The method of claim 17, wherein the one or more disease data profilesincludes one or more genotype calls associated with one or morediseases, and wherein diagnosing the disease condition additionallycomprises comparing the at least one genotype call of the nucleic acidsample with the one or more genotype calls associated with one or morediseases.
 20. The method of claim 16, wherein the one or more diseasedata profiles include losses of heterozygosity for one or more genotypecalls.